The most common historical methods and processes for reducing peak electric demand involve controlling heating, cooling, or water heating in customer facilities. These control operations may include curtailing, cycling, reduction and/or periodic cessation of particular uses. These control operations are typically performed through voluntary action by customers or through voluntary participation in a utility controlled program.
In prior systems, these control operations were conducted by one way signaling of pre-specified on and off cycles. The commands to cycle on or off were typically directed by the utility. In a typical situation, a single, uniformly-applied dispatching strategy would have been issued. For example, air conditioners may have been instructed to cycle air conditioners off for 30 minutes of 60 possible minutes each hour over the course of the next 4 to 6 hours. These instructions would typically occur during times of extreme peak loads on the utility system. Frequently, customers chose a level of cycling prior to any actual events. For instance, in the previous example air conditioners are cycled off for 50% of an hour. Customers could also choose to cycle off for 75% of an hour, for example, or 45 minutes off out of 60 possible minutes.
During this controlled cycling, energy was reduced during the time of the interruptions, unless the customer's natural on/off cycling of that appliance was less than the utility's desired control. The utility, however, would not consider the relative cycling schedules for similarly situated homes along the same circuit. The utility would not optimize cycling of customers relative to one another along a circuit, nor tie the cycling directly to the intra-hour peak demand of the utility, the localized or customer-specific cost to serve, or the characteristics and avoided costs related to the customers' location on a circuit.
With the advent of “smart grid” technologies, also called “smart home”, “smart meter”, or “home area network” (HAN) technologies, optimized demand reductions became possible at the end use or appliance level. Smart grid technologies provided the ability to capture real-time or near-real-time end-use data and enabled two-way communication. Smart grid technologies currently exist for at least some percentage of a utility's customer base.
Using smart grid technologies, a system operator can optimally and dynamically dispatch on and off signaling to specific appliances at a customer location given the observed and forecast loads of other appliances on a circuit or system. In these systems, optimally dispatched appliances, end-uses or vehicle loads differ from traditionally dispatched utility supply assets in that traditional supply assets have historically been dispatched based on aggregate-level or system-wide least cost operational principles. The key differences between dispatching supply assets and dispatching appliances are highlighted below.
First, the forced change in an appliance's duty or “on” cycle, via traditional one way signaling, ignored the operations and scheduling of other appliance loads on a circuit. Often, a utility system peak is realized when end-uses, otherwise randomly operating without central control, happen to co-occur or run at the same time during a short period of time. Rather than build supply capacity to meet these randomly occurring events, needs exists to more intelligently choreograph or manage these end uses, relative to each other, yet still provide the desired power and energy to customers such that their comfort, convenience or needs are not compromised. Needs exist for systems that optimally dispatch, schedule and manage how and when these appliance and end uses use energy, conditioned on the observed and forecasted usage of other appliances, such that the overall utility peak and system cost to serve all customers is minimized. Within traditional supply side resource dispatching frameworks, the customer load is given, and supply side resources are dispatched to accommodate this load, without regard for local cost to serve, or the ability to dispatch customer end uses, relative to each other, given a local marginal cost or specific needs or conditions exhibited along the circuit.
Second, traditional supply dispatching decisions valued only the marginal cost changes caused by aggregate supply changes and aggregate demand reduction of one-way signaling. Needs exist for systems that enable the active participation of demands into the supply analysis. Furthermore, if large enough loads are available, needs exist for systems that enable demand control to become a marginal price setter for marginal increments of “supply/demand” decisions. This may lower the marginal capacity or energy cost below the comparable, incremental unit of marginal supply. Further, traditional supply side dispatching systems operate on a single, regional price or cost to serve, often called a Locational Marginal Price or Cost (LMP). This price determines which supply side resources to dispatch within a region. However, this price necessarily reflects an average price within a region, not the local marginal cost to serve a given customer or given end use, nor considerations inherent within the energy distribution system.
Third, in the case of adding a supply side resource, energy still must be transmitted, distributed, and voltage adjusted in the delivery of electrons from a centralized plant to the customer's site. Needs exist for systems which incorporate, and optimally dispatch loads given these distribution costs, and adjust the value obtained from each customer site based on the forecasted losses, distribution costs, or voltage improvements, incurred for each customer, load and day-type. Traditional supply-oriented dispatching systems do not consider, or incorporate, distribution level cost benefits or risks within their dispatching decisions. Similarly, grid-based distribution management systems do not include supply side energy costs in their control systems which attend more toward voltage, reactive power, power factor, primary line losses or capacity inadequacies. As such, needs exist for a more focused attention on integrative systems that incorporate the value, costs and risks inherent in both the supply side and the distribution of electricity through the balanced consideration of demand side and supply side costs, the actual and forecasted cost to serve each home or businesses, and the more precise marginal cost and dispatching decisions, which supply dispatching methods alone cannot achieve.
Historically, previous demand reduction methods have ignored many demand-specific issues and impacts, micro-level marginal cost and value factors (e.g., marginal costs at the networked bus, primary losses, secondary losses, voltage benefits, power factor benefits, deferred localized distribution capacity additions, coordinated load control and scheduling across a circuit to levelize load), customer decision variables (e.g., comfort constraints, price-setting options, over-ride flexibility, behavioral predictions regarding appliance use, desire for bill stability, electric vehicle charging convenience and cost, solar, wind, other distributed generation additions), and the important changes in the marginal cost of supply resources that occur as more and more demand side options or distributed resources are adopted, by customers. Needs exist for systems that permit the inclusion and consideration of more complex, robust and more customer-focused and location-focused sources for managing the supply/demand/delivery energy balance, which reflects both price and non-price customer behavior influences, in addition to the traditional options.
Needs exist for systems that provide near real-time appliance control and coordination not only relative to each other, to achieve least cost operational utility needs, but also relative to emerging resources such as wind, solar, storage batteries, distributed generation or electric vehicles, among others. Here, these emerging resources are often characterized by many units dispersed locally, in contrast to traditional supply resources which are more centralized and larger. Given the increasing emergence of these smaller, localized, widely distributed resources, the importance and value of coordinated dispatch, of load-leveling subject to energy reductions, or of dispatching these distributed resources in an optimal least cost manner becomes increasingly important. The development of microgrid-specific algorithms that incorporate the real-time coordination of dispatchable customers' loads, over widely dispersed locales in greater number, and in conjunction with distributed storage (stand alone batteries and/or batteries in plug-in hybrid electric vehicles) and distributed generation, including, but not limited to, renewable sources, requires more comprehensive and sophisticated dispatching solutions and systems to accommodate these emerging complexities.
Needs exist for systems that provide more automated control and support to customers, such that they can participate in energy conserving behaviors without requiring them to continually attend to utility-issued hourly, daily or other time of use price signals. Traditionally, utility sponsored time of use pricing promotions do not exhibit wide participation among customers, or require frequent monitoring by customers, to achieve bill savings or energy reductions. Needs exist for systems which enable customers to set desired bill savings, desired energy reduction, and then not be required to attend to these settings continually. Rather, any perceived reduction in comfort, convenience or savings achieved can be overridden or changed at any time that it is noticed or desirably to change these settings, but that needed systems are constructed such that customers are able to gain the benefits of cost savings and comfort control, without having to constantly monitor the system.
Needs exist for customers to reduce the natural volatility in their monthly electricity bills, primarily caused by varying weather conditions from month to month. Systems are desired that are able to lock in a targeted bill amount for a given time period, and either directly control customer appliances to achieve that end, or issue messages and communication to customers regarding their progress, or lack thereof, against this targeted bill, during that time period.
With the rapid development of smart grid technologies, consumers will likely be faced with time differentiated rates for energy and a bewildering array of dispatchable and smart appliances. Many customers may have an Energy Management System (EMS) that simply allows the customer to control appliances. Intelligent management of the appliances will not be automated, but will be instead left to the customer. Needs exist for systems and methods for incorporating optimization and forecasting techniques into an EMS to allow the customer to optimally manage their energy usage. Needs also exist for decentralized processing units conducting necessary communication and analytic routines at customer locations, which optimize a customer location only, with the energy provider having a passive role.